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"I'm sure there's a lot of people at Meta, including perhaps Alex, who would like me to not tell the world that LLMs basically are a dead end when it comes to s

"I'm sure there's a lot of people at Meta, including perhaps Alex, who would like me to not tell the world that LLMs basically are a dead end when it comes to superintelligence" - Yann LeCun I've been following Yann for years and in my opinion he's been consistently right. He's been saying something like this for a long time while Elon Musk and others breathlessly broadcast that scaling up would soon get us to AGI and beyond. Mark Zuckerberg bought in to Musk's idea. We'll see, but it's increasingly looking like LeCunn is right.

aspenmartin 9mo ago
More like Yann had a long time to prove out his ideas and he did not deliver, meanwhile the industry passed Meta/Facebook by due to the sort of product-averse comfortable academic bubble that FAIR lived in. It wasn’t Zuckerberg getting swindled it was giving up on ever seeing Yann deliver anything other than LinkedIn posts and small scale tests. You do not want to bank on Yann for a big payoff. His ideas may or may not be right (joint predictive architectures, world modeling, etc), but you’d better not have him at the helm of something you expect to turn a profit on. Also almost everyone agrees the current architecture and paradigm, where you have a finite context (or a badly compressed one in Mamba / SSM), is not sufficient. That plus lots of other issues. That said scaling has delivered a LOT and it’s hard to argue against demonstrated progress.
- labrador 9mo ago
  As I said in my cousin comment, it depends on how you define AGI and ASI. Claude Opus 4.5 tells me "[Yann LeCun] thinks the phrase AGI should be retired and replaced by "human-level AI." which supports my cousin comment
- CharlieDigital 9mo ago
  > ...but you’d better not have him at the helm of something you expect to turn a profit on I don't understand this distinction. Is anyone (besides NVDA) turning a profit on inference at this point?
  - aspenmartin 9mo ago
    I don’t know I assume not but everyone has a product that could easily be profitable, it would just be dumb to do it because you will lose out to everyone else running at a loss to capture market share. I just mean the guy seems to have an aversion to business sensibility generally. I think he’s really in it for the love of the research. He’s of course rightly lauded for everything he’s done, he’s extremely brilliant, and in person (at a distance) very kind and reasonable (something that is very different than his LinkedIn personality which is basically a daily pissing contest). But I would not give him one cent of investment personally.
throw310822 9mo ago
> He's been saying something like this for a long time [...] it's increasingly looking like LeCunn is right. No? LLMs are getting smarter and smarter, only three years have passed since ChatGPT was released and we have models generating whole apps, competently working on complex features, solving math problems at a level only reached by a small percentage of the population, and much more. The progress is constant and the results are stunning. Really it makes me wonder in what sort of denial are those who think this has been proven to be a dead end.
- labrador 9mo ago
  If you call that AGI as many do or ASI, then we are not talking about the same thing. I'm talking about conversing with AI and being unable to tell if it's human or not in kind of a Turing Plus test. Turing Plus 9 would be 90% of humans can't tell if it's human or not. We're at Turing Plus 1. I can easily tell Claude Opus 4..5 is a machine by the mistakes it made. It's dumb as a box of rocks. That's how I define AGI and beyond to ASI
  - rvz 9mo ago
    This goes for any experienced senior SWE individual with a sharp attention to detail can easily tell if an AI wrote a project or not. Right now the definition of AGI has been hijacked so much that it can mean absolutely anything.
    - nutjob2 9mo ago
      No one has even given a rigorous definition of AGI. A prime environment for snake oil salesmen like Altman and Musk.
      - dragonwriter 9mo ago
        > No one has even given a rigorous definition of AGI. No one has even given a rigorous definition of the I, much less the G qualifier.
rvz 9mo ago
We are due for much more optimizations and new deep learning architectures rather than throwing more compute + RAM + money + GPUs + data at the problem, which you can do only for so long until a bottleneck occurs. Given that we have seen research from DeepSeek and Google on optimizing parts of the lower layers of deep neural networks, it's clear that a new form of AI needs to be created and I agree that LeCun will be proven right. Instead of borrowing tens of trillions to scale to a false "AGI".
- labrador 9mo ago
  This seems so obvious to me that scaling advocates seem to be exhibiting a form of magical thinking
  - djmips 9mo ago
    I can see why it's so intoxicating though, it seemed magical that scaling got us as far as it did.
skybrian 9mo ago
It's too soon to say anything like that is proven. Sure, AGI hasn't been reached yet. I suspect there's some new trick that's needed. But the work going into LLM's might be part of the eventual solution.
catigula 9mo ago
> but it's increasingly looking like LeCunn is right. This is an absolutely crazy statement vis-a-vis reality and the fact that it’s so upvoted is an indictment of the type of wishful thinking that has grown deep roots here.
- D-Machine 9mo ago
  If you are paying attention to actual research, guarded benchmarks, and understand how benchmarks are being gamed, I would say there is plenty of evidence we are approaching a clear plateau / the march-of-nines thesis of Karpathy is basically correct long-term. Short-term it remains to be seen how much more we can do with the current tech.
  - gbnwl 9mo ago
    Can you point me to some of the actual research you're talking about? I'd love to read.
    - D-Machine 9mo ago
      Your best bet would be to look deeply into performance on ARC-AGI fully-private test set performances (e.g. https://arcprize.org/blog/arc-prize-2025-results-analysis https://arcprize.org/blog/arc-prize-2025-results-analysis), and think carefully about the discrepancies here, or, just to broadly read any academic research on classic benchmarks and note the plateaus on classic datasets. It is very clear when you look at academic papers actually targeting problems specific to reasoning / intelligence (e.g. rotation invariance in images, adversarial robustness) that all the big companies are doing is just fitting more data / spending more resources on human raters and other things to boost performance on (open) metrics, but that clear actual gains in genuine intelligence are being made only by milking what we know very well to be a limited approach. I.e. there are trivially-basic problems that cannot be solved by curve-fitting models, which makes it clear most current advances are indeed coming from curve(manifold) fitting. It just isn't clear how far we can exploit these current approaches and in what domains this kind of exploitation is more than good enough. EDIT: Are people unaware Google Scholar is a thing? It is trivial to find modern AI papers that can be read without requiring access to a research institution. And e.g. HuggingFace collects trending papers (https://huggingface.co/papers/trending https://huggingface.co/papers/trending), and etc.
stevenhuang 9mo ago
> I've been following Yann for years and in my opinion he's been consistently right Lol. This is the complete opposite of reality. You realize lecun is memed for all his failed assertions of what LLMs cannot do? Look it up. You clearly have not been following closely, at all.
- labrador 9mo ago
  I meant he's held the line against those warning us about superintelligent AI soon destroying us all
  - stevenhuang 9mo ago
    Sure and that is fair. Seldom are extreme viewpoints likely scenarios anyways, but my disagreement with him stems from his unwarranted confidence in his own abilities to predict the future when he's already wrong about LLMs. He has zero epistemic humility. We don't know the nature of intelligence. His difficulties in scaling up his research is a testament to this fact. This means we really have no theoretical basis upon which to rest the claim that superintelligence cannot in principle emerge from LLM adjacent architectures--how can we make such a statement, when we don't even know what such thing looks like? We could be staring at an imperative definition of superintelligence and not know it, nevermind that approximations to such a function could in principle be learned by LLMs (universal approximation theorem). It sounds exceedingly unlikely, but would you rather be comforted by false confidence or be told the honest truth of what our current understanding of the sciences can tell us?

aspenmartin 9mo ago

More like Yann had a long time to prove out his ideas and he did not deliver, meanwhile the industry passed Meta/Facebook by due to the sort of product-averse comfortable academic bubble that FAIR lived in. It wasn’t Zuckerberg getting swindled it was giving up on ever seeing Yann deliver anything other than LinkedIn posts and small scale tests. You do not want to bank on Yann for a big payoff. His ideas may or may not be right (joint predictive architectures, world modeling, etc), but you’d better not have him at the helm of something you expect to turn a profit on. Also almost everyone agrees the current architecture and paradigm, where you have a finite context (or a badly compressed one in Mamba / SSM), is not sufficient. That plus lots of other issues. That said scaling has delivered a LOT and it’s hard to argue against demonstrated progress.

labrador 9mo ago
As I said in my cousin comment, it depends on how you define AGI and ASI. Claude Opus 4.5 tells me "[Yann LeCun] thinks the phrase AGI should be retired and replaced by "human-level AI." which supports my cousin comment
CharlieDigital 9mo ago
> ...but you’d better not have him at the helm of something you expect to turn a profit on I don't understand this distinction. Is anyone (besides NVDA) turning a profit on inference at this point?
- aspenmartin 9mo ago
  I don’t know I assume not but everyone has a product that could easily be profitable, it would just be dumb to do it because you will lose out to everyone else running at a loss to capture market share. I just mean the guy seems to have an aversion to business sensibility generally. I think he’s really in it for the love of the research. He’s of course rightly lauded for everything he’s done, he’s extremely brilliant, and in person (at a distance) very kind and reasonable (something that is very different than his LinkedIn personality which is basically a daily pissing contest). But I would not give him one cent of investment personally.

throw310822 9mo ago

> He's been saying something like this for a long time [...] it's increasingly looking like LeCunn is right. No? LLMs are getting smarter and smarter, only three years have passed since ChatGPT was released and we have models generating whole apps, competently working on complex features, solving math problems at a level only reached by a small percentage of the population, and much more. The progress is constant and the results are stunning. Really it makes me wonder in what sort of denial are those who think this has been proven to be a dead end.

labrador 9mo ago
If you call that AGI as many do or ASI, then we are not talking about the same thing. I'm talking about conversing with AI and being unable to tell if it's human or not in kind of a Turing Plus test. Turing Plus 9 would be 90% of humans can't tell if it's human or not. We're at Turing Plus 1. I can easily tell Claude Opus 4..5 is a machine by the mistakes it made. It's dumb as a box of rocks. That's how I define AGI and beyond to ASI
- rvz 9mo ago
  This goes for any experienced senior SWE individual with a sharp attention to detail can easily tell if an AI wrote a project or not. Right now the definition of AGI has been hijacked so much that it can mean absolutely anything.
  - nutjob2 9mo ago
    No one has even given a rigorous definition of AGI. A prime environment for snake oil salesmen like Altman and Musk.
    - dragonwriter 9mo ago
      > No one has even given a rigorous definition of AGI. No one has even given a rigorous definition of the I, much less the G qualifier.

rvz 9mo ago

We are due for much more optimizations and new deep learning architectures rather than throwing more compute + RAM + money + GPUs + data at the problem, which you can do only for so long until a bottleneck occurs. Given that we have seen research from DeepSeek and Google on optimizing parts of the lower layers of deep neural networks, it's clear that a new form of AI needs to be created and I agree that LeCun will be proven right. Instead of borrowing tens of trillions to scale to a false "AGI".

labrador 9mo ago
This seems so obvious to me that scaling advocates seem to be exhibiting a form of magical thinking
- djmips 9mo ago
  I can see why it's so intoxicating though, it seemed magical that scaling got us as far as it did.

skybrian 9mo ago

It's too soon to say anything like that is proven. Sure, AGI hasn't been reached yet. I suspect there's some new trick that's needed. But the work going into LLM's might be part of the eventual solution.

catigula 9mo ago

> but it's increasingly looking like LeCunn is right. This is an absolutely crazy statement vis-a-vis reality and the fact that it’s so upvoted is an indictment of the type of wishful thinking that has grown deep roots here.

D-Machine 9mo ago
If you are paying attention to actual research, guarded benchmarks, and understand how benchmarks are being gamed, I would say there is plenty of evidence we are approaching a clear plateau / the march-of-nines thesis of Karpathy is basically correct long-term. Short-term it remains to be seen how much more we can do with the current tech.
- gbnwl 9mo ago
  Can you point me to some of the actual research you're talking about? I'd love to read.
  - D-Machine 9mo ago
    Your best bet would be to look deeply into performance on ARC-AGI fully-private test set performances (e.g. https://arcprize.org/blog/arc-prize-2025-results-analysis https://arcprize.org/blog/arc-prize-2025-results-analysis), and think carefully about the discrepancies here, or, just to broadly read any academic research on classic benchmarks and note the plateaus on classic datasets. It is very clear when you look at academic papers actually targeting problems specific to reasoning / intelligence (e.g. rotation invariance in images, adversarial robustness) that all the big companies are doing is just fitting more data / spending more resources on human raters and other things to boost performance on (open) metrics, but that clear actual gains in genuine intelligence are being made only by milking what we know very well to be a limited approach. I.e. there are trivially-basic problems that cannot be solved by curve-fitting models, which makes it clear most current advances are indeed coming from curve(manifold) fitting. It just isn't clear how far we can exploit these current approaches and in what domains this kind of exploitation is more than good enough. EDIT: Are people unaware Google Scholar is a thing? It is trivial to find modern AI papers that can be read without requiring access to a research institution. And e.g. HuggingFace collects trending papers (https://huggingface.co/papers/trending https://huggingface.co/papers/trending), and etc.

stevenhuang 9mo ago

> I've been following Yann for years and in my opinion he's been consistently right Lol. This is the complete opposite of reality. You realize lecun is memed for all his failed assertions of what LLMs cannot do? Look it up. You clearly have not been following closely, at all.

labrador 9mo ago
I meant he's held the line against those warning us about superintelligent AI soon destroying us all
- stevenhuang 9mo ago
  Sure and that is fair. Seldom are extreme viewpoints likely scenarios anyways, but my disagreement with him stems from his unwarranted confidence in his own abilities to predict the future when he's already wrong about LLMs. He has zero epistemic humility. We don't know the nature of intelligence. His difficulties in scaling up his research is a testament to this fact. This means we really have no theoretical basis upon which to rest the claim that superintelligence cannot in principle emerge from LLM adjacent architectures--how can we make such a statement, when we don't even know what such thing looks like? We could be staring at an imperative definition of superintelligence and not know it, nevermind that approximations to such a function could in principle be learned by LLMs (universal approximation theorem). It sounds exceedingly unlikely, but would you rather be comforted by false confidence or be told the honest truth of what our current understanding of the sciences can tell us?